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Learning robust failure response for autonomous vision based flight

Dhruv Mauria Saxena, Vince Kurtz and Martial Hebert
2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 5824-5829, June, 2017

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The ability of autonomous mobile robots to react to and recover from potential failures of on-board systems is an important area of ongoing robotics research. With increasing emphasis on robust systems and long-term autonomy, mobile robots must be able to respond safely and intelligently to dangerous situations. Recent developments in computer vision have made autonomous vision based navigation possible. However, vision systems are known to be imperfect and prone to failure due to variable lighting, terrain changes, and other environmental variables. We describe a system for learning simple failure recovery maneuvers based on experience. This involves both recognizing when the vision system is prone to failure, and associating failures with appropriate responses that will most likely help the robot recover. We implement this system on an autonomous quadrotor and demonstrate that behaviors learned with our system are effective in recovering from situational perception failure, thereby improving reliability in cluttered and uncertain environments.

BibTeX Reference
title = {Learning robust failure response for autonomous vision based flight},
author = {Dhruv Mauria Saxena and Vince Kurtz and Martial Hebert},
booktitle = {2017 IEEE International Conference on Robotics and Automation (ICRA)},
keyword = {Failure Detection and Recovery, Visual-Based Navigation, Aerial Robotics},
sponsor = {ONR through "Provably-Stable Vision-Based Control of High-Speed Flight through Forests and Urban Environments" MURI},
publisher = {IEEE},
month = {June},
year = {2017},
pages = {5824-5829},